用激光雷达生成大场景高密度深度图，解决动态物体遮挡问题。
DOC-Depth: A novel approach for dense depth ground truth generation
- 基于激光雷达里程计重建稠密三维环境，自动处理动态物体遮挡。
- 在KITTI数据集上深度图密度从16.1%提升至71.2%，实现全密度标注。
- 方法高效可扩展，支持多传感器与多种环境，开源可用。
精确的深度信息对众多计算机视觉应用至关重要。然而，现有数据集采集方法无法在大规模动态环境中实现完全稠密的深度估计。本文提出DOC-Depth，一种新颖、高效且易于部署的稠密深度生成方法，适用于任意激光雷达传感器。通过激光雷达里程计重建一致的稠密三维环境后，利用我们先进的动态物体分类方法DOC，自动处理动态物体造成的遮挡问题。此外，DOC-Depth具备高速与可扩展性，支持无边界规模的数据集构建（时间与空间）。我们在KITTI数据集上验证了该方法的有效性，将深度图密度从16.1%提升至71.2%，并发布全新稠密深度标注数据集，以促进该领域的研究。同时，我们展示了在不同激光雷达传感器及多个环境下的结果。所有软件组件均已开源，供研究社区使用。
原文摘要 · Abstract (English)
Accurate depth information is essential for many computer vision applications. Yet, no available dataset recording method allows for fully dense accurate depth estimation in a large scale dynamic environment. In this paper, we introduce DOC-Depth, a novel, efficient and easy-to-deploy approach for dense depth generation from any LiDAR sensor. After reconstructing consistent dense 3D environment using LiDAR odometry, we address dynamic objects occlusions automatically thanks to DOC, our state-of-the art dynamic object classification method. Additionally, DOC-Depth is fast and scalable, allowing for the creation of unbounded datasets in terms of size and time. We demonstrate the effectiveness of our approach on the KITTI dataset, improving its density from 16.1% to 71.2% and release this new fully dense depth annotation, to facilitate future research in the domain. We also showcase results using various LiDAR sensors and in multiple environments. All software components are publicly available for the research community.
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